




'''simple docstring'''







import re
import string

import numpy as np

import datasets


lowerCAmelCase_     						=				'''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''

lowerCAmelCase_     						=				'''
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    25.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    50.0


    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    75.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results["exact_match"], 1))
    100.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]
    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    33.3

'''

lowerCAmelCase_     						=				'''
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       , _KWARGS_DESCRIPTION      )
class 	_snake_case(			datasets.Metric      ):






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Dict:






    """simple docstring"""


    return datasets.MetricInfo(
        description=_DESCRIPTION    ,		citation=_CITATION    ,		inputs_description=_KWARGS_DESCRIPTION    ,		features=datasets.Features(
            {
                'predictions': datasets.Value('string'    ,		id='sequence'    ),
                'references': datasets.Value('string'    ,		id='sequence'    ),
            }    )    ,		reference_urls=[]    ,		)






 def 						_UpperCamelCase (self       :  List[str]    ,		a       :  str    ,		a       :  Tuple    ,		a       :  Tuple=None    ,		a       :  Dict=False    ,		a       :  Dict=False    ,		a       :  List[str]=False    ,		)       ->   int:






    """simple docstring"""


    if regexes_to_ignore is not None:
       for s in regexes_to_ignore:
          A__       =     np.array([re.sub(a    ,		''    ,		a    ) for x in predictions]    )
          A__       =     np.array([re.sub(a    ,		''    ,		a    ) for x in references]    )
    else:
       A__       =     np.asarray(a    )
       A__       =     np.asarray(a    )

    if ignore_case:
       A__       =     np.char.lower(a    )
       A__       =     np.char.lower(a    )

    if ignore_punctuation:
       A__       =     string.punctuation.maketrans(''    ,		''    ,		string.punctuation    )
       A__       =     np.char.translate(a    ,		table=a    )
       A__       =     np.char.translate(a    ,		table=a    )

    if ignore_numbers:
       A__       =     string.digits.maketrans(''    ,		''    ,		string.digits    )
       A__       =     np.char.translate(a    ,		table=a    )
       A__       =     np.char.translate(a    ,		table=a    )

    A__       =     predictions == references

    return {"exact_match": np.mean(a    ) * 1_00}








'''simple docstring'''







import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase_     						=				1_6
lowerCAmelCase_     						=				3_2







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase = 16						):






   '''simple docstring'''







   A__       =     AutoTokenizer.from_pretrained('bert-base-cased'						)
   A__       =     DatasetDict(
       {
           'train': dataset['train'].select(UpperCAmelCase						),
           'validation': dataset['train'].select(UpperCAmelCase						),
           'test': dataset['validation'],
       }						)

   def tokenize_function(UpperCAmelCase						):
      # max_length=None => use the model max length (it's actually the default)
      A__       =     tokenizer(examples['sentence1']      ,examples['sentence2']      ,truncation=UpperCAmelCase      ,max_length=UpperCAmelCase						)
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      A__       =     datasets.map(
          UpperCAmelCase      ,batched=UpperCAmelCase      ,remove_columns=['idx', 'sentence1', 'sentence2']      ,)

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   A__       =     tokenized_datasets.rename_column('label'      ,'labels'						)

   def collate_fn(UpperCAmelCase						):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      A__       =     128 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         A__       =     16
      elif accelerator.mixed_precision != "no":
         A__       =     8
      else:
         A__       =     None

      return tokenizer.pad(
          UpperCAmelCase      ,padding='longest'      ,max_length=UpperCAmelCase      ,pad_to_multiple_of=UpperCAmelCase      ,return_tensors='pt'      ,)

   # Instantiate dataloaders.
   A__       =     DataLoader(
       tokenized_datasets['train']      ,shuffle=UpperCAmelCase      ,collate_fn=UpperCAmelCase      ,batch_size=UpperCAmelCase						)
   A__       =     DataLoader(
       tokenized_datasets['validation']      ,shuffle=UpperCAmelCase      ,collate_fn=UpperCAmelCase      ,batch_size=UpperCAmelCase						)

   A__       =     DataLoader(
       tokenized_datasets['test']      ,shuffle=UpperCAmelCase      ,collate_fn=UpperCAmelCase      ,batch_size=UpperCAmelCase						)

   return train_dataloader, eval_dataloader, test_dataloader







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []
   # Download the dataset
   A__       =     load_dataset('glue'      ,'mrpc'						)
   # Create our splits
   A__       =     StratifiedKFold(n_splits=int(args.num_folds						)						)
   # Initialize accelerator
   A__       =     Accelerator(cpu=args.cpu      ,mixed_precision=args.mixed_precision						)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   A__       =     config['lr']
   A__       =     int(config['num_epochs']						)
   A__       =     int(config['seed']						)
   A__       =     int(config['batch_size']						)

   A__       =     evaluate.load('glue'      ,'mrpc'						)

   # If the batch size is too big we use gradient accumulation
   A__       =     1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      A__       =     batch_size // MAX_GPU_BATCH_SIZE
      A__       =     MAX_GPU_BATCH_SIZE

   set_seed(UpperCAmelCase						)

   # New Code #
   # Create our folds:
   A__       =     kfold.split(np.zeros(datasets['train'].num_rows						)      ,datasets['train']['label']						)
   A__       =     []
   # Iterate over them
   for i, (train_idxs, valid_idxs) in enumerate(UpperCAmelCase						):
      A__  ,			A__  ,			A__       =     get_fold_dataloaders(
          UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      A__       =     AutoModelForSequenceClassification.from_pretrained('bert-base-cased'      ,return_dict=UpperCAmelCase						)

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      A__       =     model.to(accelerator.device						)

      # Instantiate optimizer
      A__       =     AdamW(params=model.parameters()      ,lr=UpperCAmelCase						)

      # Instantiate scheduler
      A__       =     get_linear_schedule_with_warmup(
          optimizer=UpperCAmelCase      ,num_warmup_steps=100      ,num_training_steps=(len(UpperCAmelCase						) * num_epochs) // gradient_accumulation_steps      ,)

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      A__  ,			A__  ,			A__  ,			A__  ,			A__       =     accelerator.prepare(
          UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

      # Now we train the model
      for epoch in range(UpperCAmelCase						):
         model.train()
         for step, batch in enumerate(UpperCAmelCase						):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device						)
            A__       =     model(**UpperCAmelCase						)
            A__       =     outputs.loss
            A__       =     loss / gradient_accumulation_steps
            accelerator.backward(UpperCAmelCase						)
            if step % gradient_accumulation_steps == 0:
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

         model.eval()
         for step, batch in enumerate(UpperCAmelCase						):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device						)
            with torch.no_grad():
               A__       =     model(**UpperCAmelCase						)
            A__       =     outputs.logits.argmax(dim=-1						)
            A__  ,			A__       =     accelerator.gather_for_metrics((predictions, batch['labels'])						)
            metric.add_batch(
                predictions=UpperCAmelCase      ,references=UpperCAmelCase      ,)

         A__       =     metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(F"""epoch {epoch}:"""      ,UpperCAmelCase						)

      # New Code #
      # We also run predictions on the test set at the very end
      A__       =     []
      for step, batch in enumerate(UpperCAmelCase						):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device						)
         with torch.no_grad():
            A__       =     model(**UpperCAmelCase						)
         A__       =     outputs.logits
         A__  ,			A__       =     accelerator.gather_for_metrics((predictions, batch['labels'])						)
         fold_predictions.append(predictions.cpu()						)
         if i == 0:
            # We need all of the test predictions
            test_references.append(references.cpu()						)
        # Use accelerator.print to print only on the main process.
      test_predictions.append(torch.cat(UpperCAmelCase      ,dim=0						)						)
      # We now need to release all our memory and get rid of the current model, optimizer, etc
      accelerator.free_memory()
   # New Code #
   # Finally we check the accuracy of our folded results:
   A__       =     torch.cat(UpperCAmelCase      ,dim=0						)
   A__       =     torch.stack(UpperCAmelCase      ,dim=0						).sum(dim=0						).div(int(args.num_folds						)						).argmax(dim=-1						)
   A__       =     metric.compute(predictions=UpperCAmelCase      ,references=UpperCAmelCase						)
   accelerator.print('Average test metrics from all folds:'      ,UpperCAmelCase						)







def 						_A      ( ):






   '''simple docstring'''







   A__       =     argparse.ArgumentParser(description='Simple example of training script.'						)
   parser.add_argument(
       '--mixed_precision'      ,type=UpperCAmelCase      ,default=UpperCAmelCase      ,choices=['no', 'fp16', 'bf16', 'fp8']      ,help='Whether to use mixed precision. Choose'
       'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
       'and an Nvidia Ampere GPU.'      ,)
   parser.add_argument('--cpu'      ,action='store_true'      ,help='If passed, will train on the CPU.'						)
   # New Code #
   parser.add_argument('--num_folds'      ,type=UpperCAmelCase      ,default=3      ,help='The number of splits to perform across the dataset'						)
   A__       =     parser.parse_args()
   A__       =     {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(UpperCAmelCase      ,UpperCAmelCase						)


if __name__ == "__main__":
    main()


